Thesis subject

MSc thesis topic: A comparative assessment of satellite-based forest disturbance alerts for Insular Southeast Asia

An alarming rate of forest disturbances strongly increases the pressure on tropical forest ecosystems. Insular Southeast Asia, home to the third largest humid tropical forests after the Amazon and the Congo Basin, performing globally important ecosystem services and providing livelihood to the regional population. While deforestation in Borneo and Sumatra are decreasing over the past years, Papua (West Papua and Papua New Guinea) is considered the next deforestation frontier.

In the past 10 years, satellite-based alert systems (e.g. GLAD alerts, Hansen et al. 2016, JJFAST alerts, Watanabe et al., 2018) have emerged as the primary tool to provide near real-time information on newly disturbed tropical forest areas. A wide range of stakeholders, including governments, NGOs, private sector actors and communities across the tropics have recognized the value of satellite-based disturbance alert products to empower sustainable land management and law enforcement actions against illegal forest activities (Lynch et al 2013, Finer et al 2018, Weisse et al 2019, Tabor and Holland 2020). Limited data availability due to persistent cloud cover in the tropics, however, limits the capacity of optical-based systems (e.g. GLAD alerts; Hansen et al, 2016).

With the new RADD (Radar for Detecting Deforestation) alerts, for the first time, high resolution (10 m) radar-based forest disturbance information (every 6 – 12 days) are provided (Reiche et al., 2021).

RADD alerts: https://nrtwur.users.earthengine.app/view/raddalert
WUR RADD website: http://radd-alert.wur.nl

An assessment on how timely and accurately the different (optical and radar-based) operational alert systems (RADD, GLAD, JJFAST) detect forest disturbances in Insular Southeast Asia has not yet been studied. Understanding their capacity and limitations to detect different types of forest disturbances is key to understand their synergetic potential:

  • small (e.g. selective logging) vs. large-scale (e.g. clear cuts for plantations)
  • wet vs. dry season changes
  • flat vs steep area

Software: Google Earth Engine and/or Python and/or R

Objectives

  • Comparative assessment of the spatial and temporal accuracy of RADD alerts, GLAD alerts (and JJFAST alerts) for (parts of) Insular Southeast Asia
  • Comparing the ability to detect different forest disturbances types

Literature

  • Reiche J, Mullissa A, Slagter B, Gou Y, Tsendbazar N-E, Odongo-Braun C, Vollrath A, Weisse MJ, Stolle F, Pickens A, Donchyts G, Clinton N, Gorelick N, and Herold M (2021). Forest Disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. https://doi.org/10.1088/1748-9326/abd0a8
  • Hansen M C, Krylov A, Tyukavina A, Potapov P V, Turubanova S, Zutta B, Ifo S, Margono B, Stolle F and Moore R 2016 Humid tropical forest disturbance alerts using Landsat data Environ. Res. Lett. 11 34008
  • Watanabe M, Koyama C N, Hayashi M, Nagatani I and Shimada M 2018 Early-stage deforestation detection in the tropics with L-band SAR IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11 2127–33 Papua Atlas: https://atlas.cifor.org/papua/
  • Global Forest Watch: www.globalforestwatch.org
  • Stephen V. Stehman, Giles M. Foody, 2019, Key issues in rigorous accuracy assessment of land cover products, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2019.05.018.
  • P. Olofsson, G.M. Foody, M. Herold, S.V. Stehman, C.E. Woodcock, M.A. Wulder, 2014 Good practices for estimating area and assessing accuracy of land change Remote Sens. Environ., 148 (2014), pp. 42-57

Requirements

  • Advanced Earth Observation course
  • Geo-scripting course (Good knowledge in scripting is an asset; e.g. R, python, java script)

Theme(s): Modelling & visualisation; Integrated Land Monitoring